ISSN (print) 0914-4935
ISSN (online) 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

Instructions to authors
English    日本語

Instructions for manuscript preparation
English    日本語


 Sensors and Materials
 1-23-3-303 Sendagi,
 Bunkyo-ku, Tokyo 113-0022, Japan
 Tel: 81-3-3827-8549
 Fax: 81-3-3827-8547

MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.

MYU Research

(proofreading and recording)

(translation service)

The Art of Writing Scientific Papers

(How to write scientific papers)
(Japanese Only)

Sensors and Materials, Volume 31, Number 12(2) (2019)
Copyright(C) MYU K.K.
pp. 4061-4068
S&M2069 Research Paper of Special Issue
Published: December 16, 2019

Method of Estimating Heatstroke Risk Using Wristwatch-type Device [PDF]

Yoshiyuki Kaiho, Seiichi Takamatsu, and Toshihito Itoh

(Received May 31, 2019; Accepted September 10, 2019)

Keywords: heatstroke, WBGT, black globe temperature, neural network, wearable device

As a method of estimating the risk of heatstroke with a wearable device, we have developed a method of calculating the wet bulb globe temperature (WBGT) by estimating the black globe temperature (Tg) only from sensors that can be mounted on a wristwatch-type device. In WBGT measurement, the conventional method requires a large sensor for measuring Tg, and it has been difficult to grasp an individual’s heatstroke risk. In this research, we proposed a method of estimating Tg using a neural network and compared the estimation accuracy for different numbers of layers and nodes. In the Tg range of 31 to 41 ℃, it was confirmed that when Tg was estimated by the fully connected neural network of three layers and 20 nodes, the regression coefficient between the measured Tg and the estimated Tg was 0.90, indicating a high accuracy.

Corresponding author: Yoshiyuki Kaiho

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Cite this article
Yoshiyuki Kaiho, Seiichi Takamatsu, and Toshihito Itoh, Method of Estimating Heatstroke Risk Using Wristwatch-type Device, Sens. Mater., Vol. 31, No. 12, 2019, p. 4061-4068.

Forthcoming Regular Issues

Forthcoming Special Issues

Special Issue on Advanced Materials on Electronic and Mechanical Devices and their Application on Sensors (5)
Guest editor, Teen-Hang Meen (National Formosa University), Wenbing Zhao (Cleveland State University), and Cheng-Fu Yang (National University of Kaohsiung)

Special Issue on Advances in Shape Memory Materials
Guest editor, Ryosuke Matsui (Aichi Institute of Technology) and Hiroyuki Miki (Tohoku University)

Special Issue on Perceptual Deep Learning in Computer Vision and its Application
Guest editor, Chih-Hsien Hsia (National Ilan University)

Special Issue on Materials, Devices, Circuits, and Analytical Methods for Various Sensors (3)
Guest editor, Chien-Jung Huang (National University of Kaohsiung), Cheng-Hsing Hsu (National United University), Ja-Hao Chen (Feng Chia University), and Wei-Ling Hsu (Huaiyin Normal University)
Conference website

Special Issue on Sensing Technologies and Their Applications (1)
Guest editor, Rey-Chue Hwang (I-Shou University)
Call for paper

Special Issue on New Trends in Smart Sensor Systems
Guest editor, Takahiro Hayashi (Kansai University)
Call for paper

Copyright(C) MYU K.K. All Rights Reserved.